Charlottesville Region HMDA

Data Source

• These data come from the Consumer Financial Protection Bureau and Federal Financial Institutions Examinations Council. All data were collected as part of the Home Mortgage Disclosure Act (HMDA).
• New data is released biannually on a year and half delay from when it was collected. New data can be downloaded from the Federal Financial Institutions Examinations Council here.
• Data from 2017 and earlier can be downloaded from the Consumer Financial Protection Bureau here
• Data presented here are from 2007-2020 (mostly tract averages across those years).

Data specifics

• The HMDA requires financial institutions to maintain, report, and publicly disclose information about mortgages.
• New data is released annually, typically in the summer months, on a year delay (2020 data was released in June of 2021).
• Areas included in these data: Accomack (51001) and Northampton County (51131)
• The data show below include only home purchase loans, meaning that mortgages for home improvement or refinancing are excluded.
• An important limitation to note is that the FFIEC allows banks to submit data with an error threshold as high as 10% for community banks and credit unions. The loan amounts and applicant income values appear prone to entry error at the individual level–a high proportion of values seemed too high to be realistic. Therefore, the tract level summaries of these variables (median loan amounts and median applicant income in the tract) should be interpreted with extreme caution.

Variable descriptions and Summaries

# meta %>% 
#   filter(su_tract == 1) %>%
#   select(varname, about) %>% as.list()

glimpse(esdat)

## Rows: 160
## Columns: 54
## $ census_tract                   <chr> "51001090100", "51001090100", "51001090…
## $ year                           <int> 2012, 2013, 2014, 2015, 2016, 2017, 201…
## $ conventional                   <int> 64, 111, 108, 90, 120, 155, 139, 166, 2…
## $ fha_insured                    <int> 1, 3, 6, 7, 6, 6, 19, 14, 20, 11, 20, 1…
## $ va_guaranteed                  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ usda_guaranteed                <int> 0, 0, 0, 2, 1, 0, 0, 1, 6, 4, 9, 13, 8,…
## $ req_preapproval                <int> 4, 3, 8, 5, 4, 8, 3, 3, 3, 0, 6, 8, 1, …
## $ noreq_preapproval              <int> 30, 43, 30, 11, 30, 19, 164, 191, 255, …
## $ originated_loans               <int> 47, 65, 68, 66, 89, 109, 103, 113, 167,…
## $ approvedApp_notAccepted        <int> 3, 4, 4, 1, 6, 3, 1, 3, 4, 1, 2, 5, 3, …
## $ app_denied                     <int> 4, 25, 21, 15, 8, 21, 15, 11, 16, 14, 1…
## $ app_withdrawn                  <int> 4, 3, 11, 8, 16, 11, 18, 28, 22, 1, 5, …
## $ fileclosed_incomplete          <int> 0, 1, 1, 3, 3, 6, 6, 4, 5, 1, 1, 4, 8, …
## $ purchased_loan                 <int> 10, 21, 12, 18, 18, 19, 24, 35, 44, 16,…
## $ denied_preapproval             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ approve_preapproval            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ appRace_AIAN                   <int> 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 2, …
## $ appRace_Asian                  <int> 0, 0, 1, 1, 1, 0, 0, 0, 4, 0, 1, 0, 1, …
## $ appRace_Black                  <int> 0, 0, 0, 1, 2, 2, 0, 1, 2, 5, 12, 10, 1…
## $ appRace_HawPI                  <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
## $ appRace_White                  <int> 53, 93, 101, 88, 110, 137, 146, 153, 19…
## $ appRace_multiracial            <int> 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 4, …
## $ appRace_missing                <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ appRace_NA                     <int> 7, 10, 6, 6, 11, 11, 12, 22, 35, 7, 11,…
## $ appEth_HispLat                 <int> 0, 1, 4, 1, 0, 4, 2, NA, NA, 0, 0, 1, 1…
## $ appMale                        <int> 37, 63, 65, 62, 86, 95, 95, 96, 128, 39…
## $ appFemale                      <int> 20, 33, 41, 36, 28, 50, 50, 61, 80, 20,…
## $ appsex_missing                 <int> 4, 13, 5, 7, 15, 13, 10, 15, 15, 4, 2, …
## $ highCost_mortgages             <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ nonHighCost_mortgages          <int> 68, 119, 117, 111, 140, 169, 60, 69, 91…
## $ firstlien_secured              <int> 58, 97, 104, 93, 121, 149, 165, 192, 25…
## $ sublien_secured                <int> 0, 1, 1, 0, 1, 1, 2, 2, 2, 0, 0, 1, 2, …
## $ median_income_000s             <dbl> 130.0, 106.5, 107.0, 110.5, 118.5, 117.…
## $ med_loan_amount_000s           <dbl> 164.0, 162.0, 143.0, 170.0, 177.0, 155.…
## $ total_apps                     <int> 68, 119, 117, 111, 140, 169, 167, 194, …
## $ overall_denial_rate            <dbl> 0.05882353, 0.21008403, 0.17948718, 0.1…
## $ perc_white_apps                <dbl> 77.94118, 78.15126, 86.32479, 79.27928,…
## $ perc_black_apps                <dbl> 0.0000000, 0.0000000, 0.0000000, 0.9009…
## $ perc_hislat_apps               <dbl> 0.0000000, 0.8403361, 3.4188034, 0.9009…
## $ white_denial_rate              <dbl> 0.05660377, 0.21505376, 0.17821782, 0.1…
## $ black_denial_rate              <dbl> NA, NA, NA, 1.0000000, 0.0000000, 0.500…
## $ hislat_denial_rate             <dbl> NA, 0.00, 0.25, 0.00, NA, 0.25, 0.00, N…
## $ median_income_accepted_app     <dbl> 134.0, 107.0, 114.0, 118.0, 119.0, 119.…
## $ loans_per_units                <dbl> 0.011573504, 0.016005910, 0.016744644, …
## $ perc_conventional              <dbl> 97.87234, 93.84615, 94.11765, 86.36364,…
## $ perc_govern_backed             <dbl> 2.127660, 6.153846, 5.882353, 13.636364…
## $ sum_mortgage_dollars_in000s    <int> 9005, 12187, 12468, 11616, 18309, 20637…
## $ perc_app_missingRace           <dbl> 11.764706, 13.445378, 7.692308, 10.8108…
## $ tract_population               <int> 2941, 2941, 2941, 2941, 2941, 2930, 293…
## $ minority_population            <dbl> 6.02, 6.02, 6.02, 6.02, 6.02, 6.62, 6.6…
## $ median_family_income           <int> 52600, 51600, 52000, 52700, 52300, 5330…
## $ tract_owner_occupied_units     <int> 1323, 1323, 1323, 1323, 1323, 1095, 109…
## $ tract_one_to_four_family_homes <int> 4061, 4061, 4061, 4061, 4061, 4158, 415…
## $ countyfips                     <chr> "001", "001", "001", "001", "001", "001…

esdat[which(esdat$year == 2020),] %>% select(total_apps, overall_denial_rate, white_denial_rate, black_denial_rate, hislat_denial_rate, perc_conventional, perc_govern_backed) %>% 
  select(where(~is.numeric(.x))) %>% 
  as.data.frame() %>% 
  stargazer(., type = "text", title = "Summary Statistics", digits = 1,
            summary.stat = c("mean", "sd", "min", "median", "max"))

## 
## Summary Statistics
## ===================================================
## Statistic           Mean  St. Dev. Min  Median Max 
## ---------------------------------------------------
## total_apps          121.3   78.7    26   105   258 
## overall_denial_rate  0.1    0.04   0.1   0.1   0.2 
## white_denial_rate    0.1    0.04   0.04  0.1   0.2 
## black_denial_rate    0.3    0.3     0    0.3    1  
## hislat_denial_rate   0.1    0.1    0.0   0.0   0.2 
## perc_conventional   70.9    11.6   53.8  68.0  88.6
## perc_govern_backed  29.1    11.6   11.4  32.0  46.2
## ---------------------------------------------------

Visual distribution

• The following distributions include data only from the year 2020.

longdat <- esdat[which(esdat$year == 2020),] %>% select(c(census_tract, total_apps, overall_denial_rate, white_denial_rate, black_denial_rate, hislat_denial_rate, perc_conventional, perc_govern_backed)) %>% pivot_longer(-census_tract, names_to = "measure", values_to = "value") 

longdat$measure <- factor(longdat$measure,
                         levels = c("total_apps", "overall_denial_rate", "white_denial_rate", "black_denial_rate", "hislat_denial_rate", "perc_conventional", "perc_govern_backed"))

longdat %>%
  ggplot(aes(x = value, fill = measure)) +
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  geom_histogram() + 
  facet_wrap(~measure, scales = "free")

meta %>% 
  filter(varname %in% c("total_apps", "overall_denial_rate", "white_denial_rate", "black_denial_rate", "hislat_denial_rate", "perc_conventional", "perc_govern_backed")) %>%
  mutate(label = paste0(varname, ": ", about)) %>% 
  select(label) %>% 
  as.list()

$label [1] “white_denial_rate: The tract denial rate for white applicants”
[2] “black_denial_rate: The tract denial rate for black applicants”
[3] “hislat_denial_rate: The tract denial rate for Hispanic or Latino applicants”
[4] “perc_conventional: The percent of approved applications that were conventional”
[5] “perc_govern_backed: The percent of approved applications that were backed by the USDA, VA, or FHA” [6] “total_apps: The total number of applications in the tract”
[7] “overall_denial_rate: The tract denial rate for all applicants”

Distribution over Time

Total number of applications

esdat %>% 
  group_by(countyfips, year) %>% 
  summarize(total_apps = sum(total_apps)) %>% 
  ggplot(aes(x = year, y = total_apps, color = countyfips)) +
  geom_line()

Application Denials

esdat %>% 
  group_by(countyfips, year) %>% 
  mutate(denial_rate = mean(na.omit(overall_denial_rate))) %>%
  summarize(denial_rate = mean(na.omit(overall_denial_rate))) %>% 
  ggplot(aes(x = year, y = denial_rate, color = countyfips)) +
  geom_line()

Mapping the data

Total number of applications in 2020

Total Applications

mapdat2020 <- mapdat[which(mapdat$year == 2020),]

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat2020$total_apps)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat2020,
              fillColor = ~pal(mapdat2020$total_apps),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat2020$GEOID, "<br>",
                             "Number of applications: ", mapdat2020$total_apps)
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat2020$total_apps), 
            title = "Total number of <br>mortgage applications <br> in 2020", opacity = 0.7)

Applications by Race: White Applicants

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat2020$appRace_White)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat2020,
              fillColor = ~pal(mapdat2020$appRace_White),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat2020$GEOID, "<br>",
                             "Number of applications: ", mapdat2020$appRace_White)
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat2020$appRace_White), 
            title = "Total 2020 mortgage<br>applications: White", opacity = 0.7)

Applications by Race: Black Applicants

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat2020$appRace_Black)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat2020,
              fillColor = ~pal(mapdat2020$appRace_Black),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat2020$GEOID, "<br>",
                             "Number of applications: ", mapdat2020$appRace_Black)
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat2020$appRace_Black), 
            title = "Total 2020 mortgage<br>applications: Black", opacity = 0.7)

Applications by Race: Asian Applicants

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat2020$appRace_Asian)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat2020,
              fillColor = ~pal(mapdat2020$appRace_Asian),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat2020$GEOID, "<br>",
                             "Number of applications: ", mapdat2020$appRace_Asian)
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat2020$appRace_Asian), 
            title = "Total 2020 mortgage<br>applications: Asian", opacity = 0.7)

Applications by Ethnicity: Hispanic Applicants

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat2020$appEth_HispLat)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat2020,
              fillColor = ~pal(mapdat2020$appEth_HispLat),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat2020$GEOID, "<br>",
                             "Number of applications: ", mapdat2020$appEth_HispLat)
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat2020$appEth_HispLat), 
            title = "Total 2020 mortgage<br>applications: Hispanic", opacity = 0.7)

Average mortage application denial rates from 2007-2020

Each of the following maps show the average denial rates for the years 2007-2020

Overall denial rate

mapdat <- mapdat %>%
  group_by(GEOID) %>%
  mutate(avg_overall_denial_rate = mean(na.omit(overall_denial_rate))) %>%
  ungroup()

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat$avg_overall_denial_rate)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat,
              fillColor = ~pal(mapdat$avg_overall_denial_rate),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat$GEOID, "<br>",
                             "Average overall app denial rate from 2007-2020: ", round(mapdat$avg_overall_denial_rate, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat$avg_overall_denial_rate), 
            title = "Average overall <br>app denial rate <br>from 2007-2020", opacity = 0.7)

Denial Rate by Race: White

mapdat <- mapdat %>%
  group_by(GEOID) %>%
  mutate(avg_white_denial_rate = mean(na.omit(white_denial_rate))) %>%
  ungroup()

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat$avg_white_denial_rate)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat,
              fillColor = ~pal(mapdat$avg_white_denial_rate),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat$GEOID, "<br>",
                             "Average White app denial rate from 2007-2020: ", round(mapdat$avg_white_denial_rate, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat$avg_white_denial_rate), 
            title = "Average White <br>app denial rate <br>from 2007-2020", opacity = 0.7)

Denial Rate by Race: Black

mapdat <- mapdat %>%
  group_by(GEOID) %>%
  mutate(avg_black_denial_rate = mean(na.omit(black_denial_rate))) %>%
  ungroup()

pal <- colorNumeric("plasma", reverse = TRUE, domain = (mapdat$avg_black_denial_rate))

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat,
              fillColor = ~pal((mapdat$avg_black_denial_rate)),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat$GEOID, "<br>",
                             "Average Black app denial rate from 2007-2020: ", round(mapdat$avg_black_denial_rate, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat$avg_black_denial_rate), 
            title = "Average Black <br>app denial rate <br>from 2007-2020", opacity = 0.7)

Denial Rate by Ethnicity: Hispanic

mapdat <- mapdat %>%
  group_by(GEOID) %>%
  mutate(avg_hislat_denial_rate = mean(na.omit(hislat_denial_rate))) %>%
  ungroup()

pal <- colorNumeric("plasma", reverse = TRUE, domain = (mapdat$avg_hislat_denial_rate))

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat,
              fillColor = ~pal((mapdat$avg_hislat_denial_rate)),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat$GEOID, "<br>",
                             "Average His/Lat app denial rate from 2007-2020: ", round(mapdat$avg_hislat_denial_rate, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat$avg_hislat_denial_rate), 
            title = "Average Hispanic/Latino <br>app denial rate <br>from 2007-2020", opacity = 0.7)

Government backed mortgages

Government back mortages are mortgages insured by the USDA, VA, or FHA

Percent of government backed loans from 2007-2020

mapdat <- mapdat %>%
  group_by(GEOID) %>%
  mutate(avg_percgovbacked = mean(na.omit(perc_govern_backed))) %>%
  ungroup()

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat$avg_percgovbacked)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat,
              fillColor = ~pal(mapdat$avg_percgovbacked),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat$GEOID, "<br>",
                             "Average % of gov-backed mortgages <br> from 2006-2020: ", round(mapdat$avg_percgovbacked, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat$avg_percgovbacked), 
            title = "Average % of <br>gov-backed mortgages <br>from 2007-2020", opacity = 0.7)

Percent of government backed loans in 2020

pal <- colorNumeric("plasma", reverse = TRUE, domain = mapdat2020$perc_govern_backed)

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = mapdat2020,
              fillColor = ~pal(mapdat2020$perc_govern_backed),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("GEOID: ", mapdat2020$GEOID, "<br>",
                             "Percent of gov-backed <br>mortages in 2020: ", round(mapdat2020$perc_govern_backed, 2))
              ) %>% 
  addLegend("bottomright", pal = pal, values = (mapdat2020$perc_govern_backed), 
            title = "Percent of <br>gov-backed <br>mortages in 2020", opacity = 0.7)

# animatemapdat <- st_as_sf(mapdat)
# 
# animatemapdat$year = as.numeric(animatemapdat$year)
# 
# animatemapdat <-  animatemapdat %>% filter_at(vars(NAME, geometry, perc_govern_backed),all_vars(!is.na(.)))

# cville1 <- 
#   ggplot(animatemapdat) +
#   geom_sf(aes(fill = perc_govern_backed), color = "black", alpha = .9, na.rm = TRUE) +
#   scale_fill_fermenter(palette = "Blues", direction = 1,   type = "seq", n.breaks = 7) +
#   theme_void() +
#   guides(fill = guide_colourbar(title.position="top", title.hjust = 0.5, barwidth = 1))  + 
#   labs(fill = "Percent of mortgages backed by gov.", title = 'Year:{frame_time}',
#        caption = "Percent of approved mortgage apps backed by the USDA, VA, or FHA") + 
#   transition_time(as.integer(year)) +
#   ease_aes('linear') 

# animate(cville1, fps = 1, nframes = 13)